Overview

Brought to you by YData

Dataset statistics

Number of variables24
Number of observations1033
Missing cells0
Missing cells (%)0.0%
Duplicate rows12
Duplicate rows (%)1.2%
Total size in memory816.3 KiB
Average record size in memory809.2 B

Variable types

Text1
Numeric13
Categorical10

Alerts

duracion_dias has constant value "1" Constant
Dataset has 12 (1.2%) duplicate rowsDuplicates
budget is highly overall correlated with net_profit and 1 other fieldsHigh correlation
budget_categoria is highly overall correlated with roi and 3 other fieldsHigh correlation
conversion_categoria is highly overall correlated with conversion_rate and 2 other fieldsHigh correlation
conversion_rate is highly overall correlated with conversion_categoria and 2 other fieldsHigh correlation
conversions is highly overall correlated with conversion_rate and 4 other fieldsHigh correlation
costo_clicks is highly overall correlated with ingresos_por_clickHigh correlation
costo_por_conversion is highly overall correlated with budget and 1 other fieldsHigh correlation
efficiency_index is highly overall correlated with roi and 4 other fieldsHigh correlation
ingresos_por_click is highly overall correlated with roi and 3 other fieldsHigh correlation
net_profit is highly overall correlated with budget and 1 other fieldsHigh correlation
revenue is highly overall correlated with revenue_categoria and 1 other fieldsHigh correlation
revenue_categoria is highly overall correlated with revenue and 1 other fieldsHigh correlation
revenue_per_dollar is highly overall correlated with budget_categoria and 2 other fieldsHigh correlation
roi is highly overall correlated with roi_categoria and 3 other fieldsHigh correlation
roi_categoria is highly overall correlated with roi and 1 other fieldsHigh correlation
roi_recalculated is highly overall correlated with budget_categoria and 2 other fieldsHigh correlation
start_month is highly overall correlated with temporada_inicioHigh correlation
temporada_inicio is highly overall correlated with start_monthHigh correlation
budget is highly skewed (γ1 = 31.72583531) Skewed
costo_por_conversion is highly skewed (γ1 = 22.89585953) Skewed
revenue_categoria is uniformly distributed Uniform
conversion_rate has 11 (1.1%) zeros Zeros
conversions has 11 (1.1%) zeros Zeros
costo_clicks has 11 (1.1%) zeros Zeros
efficiency_index has 17 (1.6%) zeros Zeros

Reproduction

Analysis started2025-05-25 17:56:09.917306
Analysis finished2025-05-25 17:56:28.583733
Duration18.67 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct1014
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size91.3 KiB
2025-05-25T19:56:28.716330image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Length

Max length53
Median length45
Mean length33.403679
Min length11

Characters and Unicode

Total characters34506
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1001 ?
Unique (%)96.9%

Sample

1st rowPublic-key multi-tasking throughput
2nd rowDe-engineered analyzing task-force
3rd rowBalanced solution-oriented Local Area Network
4th rowDistributed real-time methodology
5th rowFront-line executive infrastructure
ValueCountFrequency (%)
interface 41
 
1.2%
architecture 30
 
0.9%
open 28
 
0.8%
zero 23
 
0.7%
secured 23
 
0.7%
user 22
 
0.7%
4thgeneration 21
 
0.6%
methodology 19
 
0.6%
local 19
 
0.6%
reverse-engineered 19
 
0.6%
Other values (335) 3063
92.6%
2025-05-25T19:56:28.986450image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 4052
 
11.7%
i 2711
 
7.9%
t 2670
 
7.7%
a 2381
 
6.9%
r 2327
 
6.7%
2275
 
6.6%
n 2274
 
6.6%
o 1991
 
5.8%
l 1490
 
4.3%
d 1394
 
4.0%
Other values (45) 10941
31.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34506
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4052
 
11.7%
i 2711
 
7.9%
t 2670
 
7.7%
a 2381
 
6.9%
r 2327
 
6.7%
2275
 
6.6%
n 2274
 
6.6%
o 1991
 
5.8%
l 1490
 
4.3%
d 1394
 
4.0%
Other values (45) 10941
31.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34506
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4052
 
11.7%
i 2711
 
7.9%
t 2670
 
7.7%
a 2381
 
6.9%
r 2327
 
6.7%
2275
 
6.6%
n 2274
 
6.6%
o 1991
 
5.8%
l 1490
 
4.3%
d 1394
 
4.0%
Other values (45) 10941
31.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34506
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4052
 
11.7%
i 2711
 
7.9%
t 2670
 
7.7%
a 2381
 
6.9%
r 2327
 
6.7%
2275
 
6.6%
n 2274
 
6.6%
o 1991
 
5.8%
l 1490
 
4.3%
d 1394
 
4.0%
Other values (45) 10941
31.7%

budget
Real number (ℝ)

High correlation  Skewed 

Distinct1009
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58909.255
Minimum-10000
Maximum9999999
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size8.2 KiB
2025-05-25T19:56:29.064565image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum-10000
5-th percentile5692.246
Q124769.6
median46919.95
Q374856.71
95-th percentile95259.042
Maximum9999999
Range10009999
Interquartile range (IQR)50087.11

Descriptive statistics

Standard deviation310942.51
Coefficient of variation (CV)5.2783303
Kurtosis1015.2566
Mean58909.255
Median Absolute Deviation (MAD)25041.35
Skewness31.725835
Sum60853261
Variance9.6685242 × 1010
MonotonicityNot monotonic
2025-05-25T19:56:29.150773image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46919.95 5
 
0.5%
8082.3 4
 
0.4%
17712.98 3
 
0.3%
84643.1 3
 
0.3%
14589.75 3
 
0.3%
36800.58 3
 
0.3%
59892.4 2
 
0.2%
39291.9 2
 
0.2%
75569.28 2
 
0.2%
28964.45 2
 
0.2%
Other values (999) 1004
97.2%
ValueCountFrequency (%)
-10000 1
0.1%
1052.57 1
0.1%
1223.82 1
0.1%
1309.17 1
0.1%
1378.61 1
0.1%
1380.68 1
0.1%
1407.21 1
0.1%
1436.99 1
0.1%
1480.67 1
0.1%
1580.69 1
0.1%
ValueCountFrequency (%)
9999999 1
0.1%
100000 1
0.1%
99957.15 1
0.1%
99891.35 1
0.1%
99838.63 1
0.1%
99714.19 1
0.1%
99579.39 1
0.1%
99535.21 1
0.1%
99520.93 1
0.1%
99406.41 1
0.1%

roi
Real number (ℝ)

High correlation 

Distinct92
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53398838
Minimum-0.2
Maximum0.99
Zeros6
Zeros (%)0.6%
Negative1
Negative (%)0.1%
Memory size8.2 KiB
2025-05-25T19:56:29.244038image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum-0.2
5-th percentile0.136
Q10.31
median0.53
Q30.76
95-th percentile0.95
Maximum0.99
Range1.19
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation0.26131296
Coefficient of variation (CV)0.48936076
Kurtosis-1.1091103
Mean0.53398838
Median Absolute Deviation (MAD)0.22
Skewness0.006091722
Sum551.61
Variance0.068284465
MonotonicityNot monotonic
2025-05-25T19:56:29.339467image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9 33
 
3.2%
0.4 29
 
2.8%
0.5 28
 
2.7%
0.6 27
 
2.6%
0.3 22
 
2.1%
0.74 22
 
2.1%
0.16 18
 
1.7%
0.8 17
 
1.6%
0.1 16
 
1.5%
0.35 16
 
1.5%
Other values (82) 805
77.9%
ValueCountFrequency (%)
-0.2 1
 
0.1%
0 6
 
0.6%
0.1 16
1.5%
0.11 6
 
0.6%
0.12 11
1.1%
0.13 12
1.2%
0.14 10
1.0%
0.15 12
1.2%
0.16 18
1.7%
0.17 10
1.0%
ValueCountFrequency (%)
0.99 13
 
1.3%
0.98 8
 
0.8%
0.97 11
 
1.1%
0.96 10
 
1.0%
0.95 11
 
1.1%
0.94 13
 
1.3%
0.93 8
 
0.8%
0.92 6
 
0.6%
0.91 9
 
0.9%
0.9 33
3.2%

type
Categorical

Distinct5
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size65.3 KiB
email
291 
webinar
268 
social media
240 
podcast
233 
event
 
1

Length

Max length12
Median length7
Mean length7.5963214
Min length5

Characters and Unicode

Total characters7847
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowemail
2nd rowemail
3rd rowpodcast
4th rowwebinar
5th rowsocial media

Common Values

ValueCountFrequency (%)
email 291
28.2%
webinar 268
25.9%
social media 240
23.2%
podcast 233
22.6%
event 1
 
0.1%

Length

2025-05-25T19:56:29.423543image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T19:56:29.475925image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
ValueCountFrequency (%)
email 291
22.9%
webinar 268
21.1%
social 240
18.9%
media 240
18.9%
podcast 233
18.3%
event 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1272
16.2%
i 1039
13.2%
e 801
10.2%
m 531
 
6.8%
l 531
 
6.8%
d 473
 
6.0%
c 473
 
6.0%
o 473
 
6.0%
s 473
 
6.0%
n 269
 
3.4%
Other values (7) 1512
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7847
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1272
16.2%
i 1039
13.2%
e 801
10.2%
m 531
 
6.8%
l 531
 
6.8%
d 473
 
6.0%
c 473
 
6.0%
o 473
 
6.0%
s 473
 
6.0%
n 269
 
3.4%
Other values (7) 1512
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7847
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1272
16.2%
i 1039
13.2%
e 801
10.2%
m 531
 
6.8%
l 531
 
6.8%
d 473
 
6.0%
c 473
 
6.0%
o 473
 
6.0%
s 473
 
6.0%
n 269
 
3.4%
Other values (7) 1512
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7847
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1272
16.2%
i 1039
13.2%
e 801
10.2%
m 531
 
6.8%
l 531
 
6.8%
d 473
 
6.0%
c 473
 
6.0%
o 473
 
6.0%
s 473
 
6.0%
n 269
 
3.4%
Other values (7) 1512
19.3%

target_audience
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size60.7 KiB
b2b
532 
b2c
501 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3099
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowb2b
2nd rowb2c
3rd rowb2b
4th rowb2b
5th rowb2b

Common Values

ValueCountFrequency (%)
b2b 532
51.5%
b2c 501
48.5%

Length

2025-05-25T19:56:29.545932image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T19:56:29.585914image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
ValueCountFrequency (%)
b2b 532
51.5%
b2c 501
48.5%

Most occurring characters

ValueCountFrequency (%)
b 1565
50.5%
2 1033
33.3%
c 501
 
16.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3099
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 1565
50.5%
2 1033
33.3%
c 501
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3099
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 1565
50.5%
2 1033
33.3%
c 501
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3099
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 1565
50.5%
2 1033
33.3%
c 501
 
16.2%

channel
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size64.8 KiB
promotion
282 
referral
258 
organic
250 
paid
243 

Length

Max length9
Median length8
Mean length7.090029
Min length4

Characters and Unicode

Total characters7324
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roworganic
2nd rowpromotion
3rd rowpaid
4th roworganic
5th rowpromotion

Common Values

ValueCountFrequency (%)
promotion 282
27.3%
referral 258
25.0%
organic 250
24.2%
paid 243
23.5%

Length

2025-05-25T19:56:30.048975image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T19:56:30.096788image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
ValueCountFrequency (%)
promotion 282
27.3%
referral 258
25.0%
organic 250
24.2%
paid 243
23.5%

Most occurring characters

ValueCountFrequency (%)
r 1306
17.8%
o 1096
15.0%
i 775
10.6%
a 751
10.3%
n 532
7.3%
p 525
7.2%
e 516
 
7.0%
m 282
 
3.9%
t 282
 
3.9%
f 258
 
3.5%
Other values (4) 1001
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7324
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1306
17.8%
o 1096
15.0%
i 775
10.6%
a 751
10.3%
n 532
7.3%
p 525
7.2%
e 516
 
7.0%
m 282
 
3.9%
t 282
 
3.9%
f 258
 
3.5%
Other values (4) 1001
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7324
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1306
17.8%
o 1096
15.0%
i 775
10.6%
a 751
10.3%
n 532
7.3%
p 525
7.2%
e 516
 
7.0%
m 282
 
3.9%
t 282
 
3.9%
f 258
 
3.5%
Other values (4) 1001
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7324
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1306
17.8%
o 1096
15.0%
i 775
10.6%
a 751
10.3%
n 532
7.3%
p 525
7.2%
e 516
 
7.0%
m 282
 
3.9%
t 282
 
3.9%
f 258
 
3.5%
Other values (4) 1001
13.7%

conversion_rate
Real number (ℝ)

High correlation  Zeros 

Distinct92
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54208132
Minimum0
Maximum1.5
Zeros11
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-05-25T19:56:30.169932image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.13
Q10.3
median0.55
Q30.77
95-th percentile0.95
Maximum1.5
Range1.5
Interquartile range (IQR)0.47

Descriptive statistics

Standard deviation0.26673017
Coefficient of variation (CV)0.49204827
Kurtosis-1.0481051
Mean0.54208132
Median Absolute Deviation (MAD)0.23
Skewness-0.025634052
Sum559.97
Variance0.071144986
MonotonicityNot monotonic
2025-05-25T19:56:30.252679image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 27
 
2.6%
0.7 27
 
2.6%
0.2 24
 
2.3%
0.9 21
 
2.0%
0.8 19
 
1.8%
0.1 18
 
1.7%
0.6 18
 
1.7%
0.3 17
 
1.6%
0.65 17
 
1.6%
0.85 17
 
1.6%
Other values (82) 828
80.2%
ValueCountFrequency (%)
0 11
1.1%
0.1 18
1.7%
0.11 6
 
0.6%
0.12 13
1.3%
0.13 12
1.2%
0.14 5
 
0.5%
0.15 6
 
0.6%
0.16 11
1.1%
0.17 10
1.0%
0.18 14
1.4%
ValueCountFrequency (%)
1.5 1
 
0.1%
0.99 13
1.3%
0.98 10
1.0%
0.97 13
1.3%
0.96 12
1.2%
0.95 10
1.0%
0.94 8
0.8%
0.93 6
0.6%
0.92 12
1.2%
0.91 9
0.9%

revenue
Real number (ℝ)

High correlation 

Distinct1009
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean511614.5
Minimum108.21
Maximum999712.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-05-25T19:56:30.349820image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum108.21
5-th percentile50876.016
Q1267847.44
median517944.03
Q3764590.33
95-th percentile949492.1
Maximum999712.49
Range999604.28
Interquartile range (IQR)496742.89

Descriptive statistics

Standard deviation286735.67
Coefficient of variation (CV)0.56045258
Kurtosis-1.1760746
Mean511614.5
Median Absolute Deviation (MAD)249415.34
Skewness-0.048017174
Sum5.2849778 × 108
Variance8.2217343 × 1010
MonotonicityNot monotonic
2025-05-25T19:56:30.443068image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
709593.48 4
 
0.4%
516609.1 3
 
0.3%
458227.42 3
 
0.3%
89958.73 3
 
0.3%
558302.11 3
 
0.3%
206241.46 3
 
0.3%
517944.035 3
 
0.3%
174462.47 2
 
0.2%
47511.35 2
 
0.2%
172882.59 2
 
0.2%
Other values (999) 1005
97.3%
ValueCountFrequency (%)
108.21 1
0.1%
2810.51 1
0.1%
3641.3 1
0.1%
4190.95 1
0.1%
5971.96 1
0.1%
7622.28 1
0.1%
7636.54 1
0.1%
8272.5 1
0.1%
8811.92 1
0.1%
9576.39 1
0.1%
ValueCountFrequency (%)
999712.49 1
0.1%
999317.92 1
0.1%
997657.18 1
0.1%
996578.25 1
0.1%
996493.1 1
0.1%
995340.62 1
0.1%
994306.41 1
0.1%
993906.77 1
0.1%
993317.73 1
0.1%
992544.71 1
0.1%

duracion_dias
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.6 KiB
1
1033 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1033
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1033
100.0%

Length

2025-05-25T19:56:30.516642image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T19:56:30.549962image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1033
100.0%

Most occurring characters

ValueCountFrequency (%)
1 1033
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1033
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1033
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1033
100.0%

roi_categoria
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
Medio
547 
Bajo
479 
Pérdida
 
7

Length

Max length7
Median length5
Mean length4.5498548
Min length4

Characters and Unicode

Total characters4700
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBajo
2nd rowMedio
3rd rowBajo
4th rowBajo
5th rowBajo

Common Values

ValueCountFrequency (%)
Medio 547
53.0%
Bajo 479
46.4%
Pérdida 7
 
0.7%

Length

2025-05-25T19:56:30.602747image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T19:56:30.652053image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
ValueCountFrequency (%)
medio 547
53.0%
bajo 479
46.4%
pérdida 7
 
0.7%

Most occurring characters

ValueCountFrequency (%)
o 1026
21.8%
d 561
11.9%
i 554
11.8%
e 547
11.6%
M 547
11.6%
a 486
10.3%
B 479
10.2%
j 479
10.2%
P 7
 
0.1%
é 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1026
21.8%
d 561
11.9%
i 554
11.8%
e 547
11.6%
M 547
11.6%
a 486
10.3%
B 479
10.2%
j 479
10.2%
P 7
 
0.1%
é 7
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1026
21.8%
d 561
11.9%
i 554
11.8%
e 547
11.6%
M 547
11.6%
a 486
10.3%
B 479
10.2%
j 479
10.2%
P 7
 
0.1%
é 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1026
21.8%
d 561
11.9%
i 554
11.8%
e 547
11.6%
M 547
11.6%
a 486
10.3%
B 479
10.2%
j 479
10.2%
P 7
 
0.1%
é 7
 
0.1%

conversion_categoria
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size62.0 KiB
Baja
367 
Media
336 
Alta
330 

Length

Max length5
Median length4
Mean length4.3252662
Min length4

Characters and Unicode

Total characters4468
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBaja
2nd rowMedia
3rd rowBaja
4th rowBaja
5th rowAlta

Common Values

ValueCountFrequency (%)
Baja 367
35.5%
Media 336
32.5%
Alta 330
31.9%

Length

2025-05-25T19:56:30.715195image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T19:56:30.749913image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
ValueCountFrequency (%)
baja 367
35.5%
media 336
32.5%
alta 330
31.9%

Most occurring characters

ValueCountFrequency (%)
a 1400
31.3%
B 367
 
8.2%
j 367
 
8.2%
M 336
 
7.5%
e 336
 
7.5%
d 336
 
7.5%
i 336
 
7.5%
A 330
 
7.4%
l 330
 
7.4%
t 330
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1400
31.3%
B 367
 
8.2%
j 367
 
8.2%
M 336
 
7.5%
e 336
 
7.5%
d 336
 
7.5%
i 336
 
7.5%
A 330
 
7.4%
l 330
 
7.4%
t 330
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1400
31.3%
B 367
 
8.2%
j 367
 
8.2%
M 336
 
7.5%
e 336
 
7.5%
d 336
 
7.5%
i 336
 
7.5%
A 330
 
7.4%
l 330
 
7.4%
t 330
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1400
31.3%
B 367
 
8.2%
j 367
 
8.2%
M 336
 
7.5%
e 336
 
7.5%
d 336
 
7.5%
i 336
 
7.5%
A 330
 
7.4%
l 330
 
7.4%
t 330
 
7.4%

revenue_categoria
Categorical

High correlation  Uniform 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size62.3 KiB
Poca
345 
Mucha
344 
Media
344 

Length

Max length5
Median length5
Mean length4.6660213
Min length4

Characters and Unicode

Total characters4820
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMucha
2nd rowMedia
3rd rowMedia
4th rowPoca
5th rowPoca

Common Values

ValueCountFrequency (%)
Poca 345
33.4%
Mucha 344
33.3%
Media 344
33.3%

Length

2025-05-25T19:56:30.825408image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T19:56:30.868555image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
ValueCountFrequency (%)
poca 345
33.4%
mucha 344
33.3%
media 344
33.3%

Most occurring characters

ValueCountFrequency (%)
a 1033
21.4%
c 689
14.3%
M 688
14.3%
P 345
 
7.2%
o 345
 
7.2%
u 344
 
7.1%
h 344
 
7.1%
e 344
 
7.1%
d 344
 
7.1%
i 344
 
7.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1033
21.4%
c 689
14.3%
M 688
14.3%
P 345
 
7.2%
o 345
 
7.2%
u 344
 
7.1%
h 344
 
7.1%
e 344
 
7.1%
d 344
 
7.1%
i 344
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1033
21.4%
c 689
14.3%
M 688
14.3%
P 345
 
7.2%
o 345
 
7.2%
u 344
 
7.1%
h 344
 
7.1%
e 344
 
7.1%
d 344
 
7.1%
i 344
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1033
21.4%
c 689
14.3%
M 688
14.3%
P 345
 
7.2%
o 345
 
7.2%
u 344
 
7.1%
h 344
 
7.1%
e 344
 
7.1%
d 344
 
7.1%
i 344
 
7.1%

budget_categoria
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size63.7 KiB
Muy Alto
489 
Alto
443 
Medio
100 
Bajo
 
1

Length

Max length8
Median length5
Mean length5.9903195
Min length4

Characters and Unicode

Total characters6188
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowMedio
2nd rowAlto
3rd rowMuy Alto
4th rowAlto
5th rowAlto

Common Values

ValueCountFrequency (%)
Muy Alto 489
47.3%
Alto 443
42.9%
Medio 100
 
9.7%
Bajo 1
 
0.1%

Length

2025-05-25T19:56:30.933901image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T19:56:30.990667image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
ValueCountFrequency (%)
alto 932
61.2%
muy 489
32.1%
medio 100
 
6.6%
bajo 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 1033
16.7%
A 932
15.1%
t 932
15.1%
l 932
15.1%
M 589
9.5%
u 489
7.9%
y 489
7.9%
489
7.9%
e 100
 
1.6%
d 100
 
1.6%
Other values (4) 103
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1033
16.7%
A 932
15.1%
t 932
15.1%
l 932
15.1%
M 589
9.5%
u 489
7.9%
y 489
7.9%
489
7.9%
e 100
 
1.6%
d 100
 
1.6%
Other values (4) 103
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1033
16.7%
A 932
15.1%
t 932
15.1%
l 932
15.1%
M 589
9.5%
u 489
7.9%
y 489
7.9%
489
7.9%
e 100
 
1.6%
d 100
 
1.6%
Other values (4) 103
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1033
16.7%
A 932
15.1%
t 932
15.1%
l 932
15.1%
M 589
9.5%
u 489
7.9%
y 489
7.9%
489
7.9%
e 100
 
1.6%
d 100
 
1.6%
Other values (4) 103
 
1.7%

roi_recalculated
Real number (ℝ)

High correlation 

Distinct1013
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.811023
Minimum-52.794404
Maximum884.759
Zeros0
Zeros (%)0.0%
Negative44
Negative (%)4.3%
Memory size8.2 KiB
2025-05-25T19:56:31.063255image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum-52.794404
5-th percentile0.20918943
Q14.4136417
median9.3949705
Q320.042581
95-th percentile94.989979
Maximum884.759
Range937.5534
Interquartile range (IQR)15.628939

Descriptive statistics

Standard deviation61.415862
Coefficient of variation (CV)2.4753458
Kurtosis73.14624
Mean24.811023
Median Absolute Deviation (MAD)6.5446148
Skewness7.4284549
Sum25629.787
Variance3771.9081
MonotonicityNot monotonic
2025-05-25T19:56:31.148815image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.79598382 4
 
0.4%
4.41364175 3
 
0.3%
4.604299171 3
 
0.3%
5.165885639 3
 
0.3%
28.16556672 3
 
0.3%
0.2091894258 2
 
0.2%
17.14485942 2
 
0.2%
309.1388048 2
 
0.2%
3.32083949 2
 
0.2%
1.724215144 2
 
0.2%
Other values (1003) 1007
97.5%
ValueCountFrequency (%)
-52.7944035 1
0.1%
-0.9982540607 1
0.1%
-0.9949999995 1
0.1%
-0.9597439377 1
0.1%
-0.9235587232 1
0.1%
-0.9171850215 1
0.1%
-0.9042935881 1
0.1%
-0.8862647666 1
0.1%
-0.8829940136 1
0.1%
-0.8790694943 1
0.1%
ValueCountFrequency (%)
884.7589994 1
0.1%
627.0071416 1
0.1%
624.8934652 1
0.1%
612.6218345 1
0.1%
527.2747182 1
0.1%
524.4264286 1
0.1%
323.7059312 1
0.1%
321.9708283 1
0.1%
309.1388048 2
0.2%
308.5436491 1
0.1%

start_month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.392062
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-05-25T19:56:31.219516image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4151133
Coefficient of variation (CV)0.53427413
Kurtosis-1.1822627
Mean6.392062
Median Absolute Deviation (MAD)3
Skewness-0.0073632203
Sum6603
Variance11.662999
MonotonicityNot monotonic
2025-05-25T19:56:31.276954image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 103
10.0%
9 99
9.6%
8 97
9.4%
3 96
9.3%
7 93
9.0%
4 92
8.9%
11 89
8.6%
6 83
8.0%
5 75
7.3%
12 71
6.9%
Other values (2) 135
13.1%
ValueCountFrequency (%)
1 103
10.0%
2 66
6.4%
3 96
9.3%
4 92
8.9%
5 75
7.3%
6 83
8.0%
7 93
9.0%
8 97
9.4%
9 99
9.6%
10 69
6.7%
ValueCountFrequency (%)
12 71
6.9%
11 89
8.6%
10 69
6.7%
9 99
9.6%
8 97
9.4%
7 93
9.0%
6 83
8.0%
5 75
7.3%
4 92
8.9%
3 96
9.3%

temporada_inicio
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size72.4 KiB
Verano
273 
Primavera
263 
Otoño
257 
Invierno
240 

Length

Max length9
Median length8
Mean length6.9796709
Min length5

Characters and Unicode

Total characters7210
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrimavera
2nd rowInvierno
3rd rowInvierno
4th rowOtoño
5th rowVerano

Common Values

ValueCountFrequency (%)
Verano 273
26.4%
Primavera 263
25.5%
Otoño 257
24.9%
Invierno 240
23.2%

Length

2025-05-25T19:56:31.349590image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T19:56:31.400579image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
ValueCountFrequency (%)
verano 273
26.4%
primavera 263
25.5%
otoño 257
24.9%
invierno 240
23.2%

Most occurring characters

ValueCountFrequency (%)
r 1039
14.4%
o 1027
14.2%
a 799
11.1%
e 776
10.8%
n 753
10.4%
i 503
7.0%
v 503
7.0%
V 273
 
3.8%
P 263
 
3.6%
m 263
 
3.6%
Other values (4) 1011
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7210
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1039
14.4%
o 1027
14.2%
a 799
11.1%
e 776
10.8%
n 753
10.4%
i 503
7.0%
v 503
7.0%
V 273
 
3.8%
P 263
 
3.6%
m 263
 
3.6%
Other values (4) 1011
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7210
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1039
14.4%
o 1027
14.2%
a 799
11.1%
e 776
10.8%
n 753
10.4%
i 503
7.0%
v 503
7.0%
V 273
 
3.8%
P 263
 
3.6%
m 263
 
3.6%
Other values (4) 1011
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7210
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1039
14.4%
o 1027
14.2%
a 799
11.1%
e 776
10.8%
n 753
10.4%
i 503
7.0%
v 503
7.0%
V 273
 
3.8%
P 263
 
3.6%
m 263
 
3.6%
Other values (4) 1011
14.0%

net_profit
Real number (ℝ)

High correlation 

Distinct1015
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean452705.25
Minimum-9949999
Maximum987859.73
Zeros0
Zeros (%)0.0%
Negative43
Negative (%)4.2%
Memory size8.2 KiB
2025-05-25T19:56:31.479907image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum-9949999
5-th percentile8934.176
Q1215916.76
median473021.14
Q3707633.21
95-th percentile895710.51
Maximum987859.73
Range10937859
Interquartile range (IQR)491716.45

Descriptive statistics

Standard deviation433673.06
Coefficient of variation (CV)0.95795899
Kurtosis320.03833
Mean452705.25
Median Absolute Deviation (MAD)247866.66
Skewness-13.383382
Sum4.6764452 × 108
Variance1.8807232 × 1011
MonotonicityNot monotonic
2025-05-25T19:56:31.567300image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
701511.18 4
 
0.4%
169440.88 3
 
0.3%
75368.98 3
 
0.3%
498896.12 3
 
0.3%
373584.32 3
 
0.3%
561464.08 2
 
0.2%
312438.56 2
 
0.2%
110421.1 2
 
0.2%
143918.14 2
 
0.2%
694261.88 2
 
0.2%
Other values (1005) 1007
97.5%
ValueCountFrequency (%)
-9949999 1
0.1%
-92091.91 1
0.1%
-91618.85 1
0.1%
-85765.04 1
0.1%
-83260.49 1
0.1%
-77408.2 1
0.1%
-69612.81 1
0.1%
-68489.09 1
0.1%
-67005.31 1
0.1%
-61869.89 1
0.1%
ValueCountFrequency (%)
987859.73 1
0.1%
987359.82 1
0.1%
979827.4 1
0.1%
974958.97 1
0.1%
973355.11 1
0.1%
965199.63 1
0.1%
964497.81 1
0.1%
963838 1
0.1%
960202.7 1
0.1%
958135.52 1
0.1%

conversions
Real number (ℝ)

High correlation  Zeros 

Distinct1003
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean277986.72
Minimum0
Maximum964632.77
Zeros11
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size8.2 KiB
2025-05-25T19:56:31.658633image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16239.157
Q197422.044
median216111.87
Q3419348.79
95-th percentile692911.43
Maximum964632.77
Range964632.77
Interquartile range (IQR)321926.75

Descriptive statistics

Standard deviation219885.1
Coefficient of variation (CV)0.7909914
Kurtosis-0.14539902
Mean277986.72
Median Absolute Deviation (MAD)148466.33
Skewness0.80911707
Sum2.8716028 × 108
Variance4.8349459 × 1010
MonotonicityNot monotonic
2025-05-25T19:56:31.750543image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
1.1%
283837.392 4
 
0.4%
340962.006 3
 
0.3%
128303.6776 3
 
0.3%
17092.1587 3
 
0.3%
374062.4137 3
 
0.3%
107245.5592 3
 
0.3%
38484.1935 2
 
0.2%
29390.0403 2
 
0.2%
345335.2072 2
 
0.2%
Other values (993) 997
96.5%
ValueCountFrequency (%)
0 11
1.1%
83.3217 1
 
0.1%
419.095 1
 
0.1%
758.8377 1
 
0.1%
1374.5772 1
 
0.1%
1436.4585 1
 
0.1%
2533.2462 1
 
0.1%
2661.8471 1
 
0.1%
3568.474 1
 
0.1%
3583.176 1
 
0.1%
ValueCountFrequency (%)
964632.7746 1
0.1%
954051.8638 1
0.1%
935379.7945 1
0.1%
921771.3204 1
0.1%
915375.236 1
0.1%
907059.9884 1
0.1%
899700.0163 1
0.1%
894373.9296 1
0.1%
888263.5488 1
0.1%
886375.4536 1
0.1%

costo_por_conversion
Real number (ℝ)

High correlation  Skewed 

Distinct1005
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5805041
Minimum-0.19307105
Maximum999.9999
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size8.2 KiB
2025-05-25T19:56:31.838276image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum-0.19307105
5-th percentile0.016749864
Q10.086898155
median0.18806696
Q30.50209238
95-th percentile2.3844049
Maximum999.9999
Range1000.193
Interquartile range (IQR)0.41519422

Descriptive statistics

Standard deviation39.020732
Coefficient of variation (CV)15.121361
Kurtosis540.04787
Mean2.5805041
Median Absolute Deviation (MAD)0.13575584
Skewness22.89586
Sum2665.6607
Variance1522.6176
MonotonicityNot monotonic
2025-05-25T19:56:31.932902image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1880669594 11
 
1.1%
0.02847510662 4
 
0.4%
0.05195001111 3
 
0.3%
0.6597090713 3
 
0.3%
0.8535931743 3
 
0.3%
0.3431431593 3
 
0.3%
1.020988006 2
 
0.2%
0.2020231845 2
 
0.2%
0.9855192339 2
 
0.2%
0.117259634 2
 
0.2%
Other values (995) 998
96.6%
ValueCountFrequency (%)
-0.1930710526 1
0.1%
0.001873181857 1
0.1%
0.002138444974 1
0.1%
0.00242078181 1
0.1%
0.002565852872 1
0.1%
0.003505471279 1
0.1%
0.003793367905 2
0.2%
0.004627578835 1
0.1%
0.004681974146 1
0.1%
0.00524825398 1
0.1%
ValueCountFrequency (%)
999.9999 1
0.1%
743.8410402 1
0.1%
92.00362607 1
0.1%
87.92350183 1
0.1%
55.12808062 1
0.1%
39.41134107 1
0.1%
30.12062564 1
0.1%
19.34929765 1
0.1%
18.68848204 1
0.1%
17.28038749 1
0.1%

costo_clicks
Real number (ℝ)

High correlation  Zeros 

Distinct1005
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30510.993
Minimum-1555389.3
Maximum1012627.9
Zeros11
Zeros (%)1.1%
Negative44
Negative (%)4.3%
Memory size8.2 KiB
2025-05-25T19:56:32.022916image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum-1555389.3
5-th percentile0
Q18884.4711
median22712.27
Q347599.262
95-th percentile98001.291
Maximum1012627.9
Range2568017.2
Interquartile range (IQR)38714.791

Descriptive statistics

Standard deviation81533.264
Coefficient of variation (CV)2.6722586
Kurtosis176.07458
Mean30510.993
Median Absolute Deviation (MAD)16797.336
Skewness-6.8631298
Sum31517856
Variance6.6476732 × 109
MonotonicityNot monotonic
2025-05-25T19:56:32.116702image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11
 
1.1%
3270.167346 4
 
0.4%
12105.63272 3
 
0.3%
29069.79879 3
 
0.3%
3308.659907 3
 
0.3%
23292.48279 3
 
0.3%
183968.1588 2
 
0.2%
58557.49914 2
 
0.2%
5914.93437 2
 
0.2%
20142.20115 2
 
0.2%
Other values (995) 998
96.6%
ValueCountFrequency (%)
-1555389.313 1
0.1%
-816535.6726 1
0.1%
-604843.2316 1
0.1%
-489640.031 1
0.1%
-329231.6542 1
0.1%
-234385.6043 1
0.1%
-86746.51062 1
0.1%
-63437.56624 1
0.1%
-57373.76948 1
0.1%
-57115.63886 1
0.1%
ValueCountFrequency (%)
1012627.916 1
0.1%
471805.9228 1
0.1%
439058.6918 1
0.1%
281652.9601 1
0.1%
275237.0283 1
0.1%
274254.9181 1
0.1%
265316.6383 1
0.1%
250937.3962 1
0.1%
234409.5767 1
0.1%
203779.3703 1
0.1%

ingresos_por_click
Real number (ℝ)

High correlation 

Distinct1005
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.228017
Minimum-527.94403
Maximum3219.7083
Zeros0
Zeros (%)0.0%
Negative44
Negative (%)4.3%
Memory size8.2 KiB
2025-05-25T19:56:32.204683image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum-527.94403
5-th percentile0.30023777
Q17.7986653
median19.670162
Q346.100257
95-th percentile217.94931
Maximum3219.7083
Range3747.6523
Interquartile range (IQR)38.301591

Descriptive statistics

Standard deviation191.29581
Coefficient of variation (CV)3.0254912
Kurtosis107.63287
Mean63.228017
Median Absolute Deviation (MAD)14.457047
Skewness8.8818998
Sum65314.541
Variance36594.086
MonotonicityNot monotonic
2025-05-25T19:56:32.291859image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.67016197 11
 
1.1%
216.9899595 4
 
0.4%
42.67510109 3
 
0.3%
15.76300625 3
 
0.3%
27.18887178 3
 
0.3%
8.854421483 3
 
0.3%
0.2582585504 2
 
0.2%
9.534254676 2
 
0.2%
29.22815017 2
 
0.2%
36.4784243 2
 
0.2%
Other values (995) 998
96.6%
ValueCountFrequency (%)
-527.944035 1
0.1%
-8.862647666 1
0.1%
-6.347120529 1
0.1%
-5.860463295 1
0.1%
-4.974999997 1
0.1%
-4.772425708 1
0.1%
-3.769473065 1
0.1%
-3.554607177 1
0.1%
-3.536280363 1
0.1%
-3.028667363 1
0.1%
ValueCountFrequency (%)
3219.708283 1
0.1%
2090.023805 1
0.1%
2010.815908 1
0.1%
1904.152536 1
0.1%
1256.362451 1
0.1%
1225.060608 1
0.1%
1141.698588 1
0.1%
984.4838948 1
0.1%
976.4346633 1
0.1%
946.8082805 1
0.1%

revenue_per_dollar
Real number (ℝ)

High correlation 

Distinct1013
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.811023
Minimum-51.794404
Maximum885.759
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)0.1%
Memory size8.2 KiB
2025-05-25T19:56:32.382879image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum-51.794404
5-th percentile1.2091894
Q15.4136417
median10.394971
Q321.042581
95-th percentile95.989979
Maximum885.759
Range937.5534
Interquartile range (IQR)15.628939

Descriptive statistics

Standard deviation61.415862
Coefficient of variation (CV)2.3794431
Kurtosis73.14624
Mean25.811023
Median Absolute Deviation (MAD)6.5446148
Skewness7.4284549
Sum26662.787
Variance3771.9081
MonotonicityNot monotonic
2025-05-25T19:56:32.474174image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87.79598382 4
 
0.4%
5.41364175 3
 
0.3%
5.604299171 3
 
0.3%
6.165885639 3
 
0.3%
29.16556672 3
 
0.3%
1.209189426 2
 
0.2%
18.14485942 2
 
0.2%
310.1388048 2
 
0.2%
4.32083949 2
 
0.2%
2.724215144 2
 
0.2%
Other values (1003) 1007
97.5%
ValueCountFrequency (%)
-51.7944035 1
0.1%
0.001745939291 1
0.1%
0.0050000005 1
0.1%
0.04025606231 1
0.1%
0.07644127681 1
0.1%
0.08281497847 1
0.1%
0.09570641194 1
0.1%
0.1137352334 1
0.1%
0.1170059864 1
0.1%
0.1209305057 1
0.1%
ValueCountFrequency (%)
885.7589994 1
0.1%
628.0071416 1
0.1%
625.8934652 1
0.1%
613.6218345 1
0.1%
528.2747182 1
0.1%
525.4264286 1
0.1%
324.7059312 1
0.1%
322.9708283 1
0.1%
310.1388048 2
0.2%
309.5436491 1
0.1%

efficiency_index
Real number (ℝ)

High correlation  Zeros 

Distinct760
Distinct (%)73.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29219661
Minimum-0.02
Maximum1.2
Zeros17
Zeros (%)1.6%
Negative1
Negative (%)0.1%
Memory size8.2 KiB
2025-05-25T19:56:32.562866image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum-0.02
5-th percentile0.0396
Q10.1139
median0.234
Q30.4232
95-th percentile0.7284
Maximum1.2
Range1.22
Interquartile range (IQR)0.3093

Descriptive statistics

Standard deviation0.21836282
Coefficient of variation (CV)0.74731468
Kurtosis0.049261553
Mean0.29219661
Median Absolute Deviation (MAD)0.138
Skewness0.89005456
Sum301.8391
Variance0.04768232
MonotonicityNot monotonic
2025-05-25T19:56:32.650112image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17
 
1.6%
0.204 6
 
0.6%
0.36 6
 
0.6%
0.14 6
 
0.6%
0.096 6
 
0.6%
0.112 5
 
0.5%
0.168 5
 
0.5%
0.16 4
 
0.4%
0.0396 4
 
0.4%
0.252 4
 
0.4%
Other values (750) 970
93.9%
ValueCountFrequency (%)
-0.02 1
 
0.1%
0 17
1.6%
0.016 1
 
0.1%
0.0168 1
 
0.1%
0.017 1
 
0.1%
0.019 2
 
0.2%
0.02 2
 
0.2%
0.0208 1
 
0.1%
0.0216 1
 
0.1%
0.0247 1
 
0.1%
ValueCountFrequency (%)
1.2 1
0.1%
0.9603 1
0.1%
0.9506 1
0.1%
0.891 1
0.1%
0.8835 1
0.1%
0.882 2
0.2%
0.8736 1
0.1%
0.873 1
0.1%
0.8712 1
0.1%
0.864 1
0.1%

theme
Categorical

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size99.6 KiB
Procesos/Metodología
282 
Arquitectura/Infraestructura
139 
Usuario/Cliente
131 
Red/Conectividad
97 
Tecnología/IT
78 
Other values (5)
306 

Length

Max length28
Median length20
Mean length18.524685
Min length9

Characters and Unicode

Total characters19136
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUsuario/Cliente
2nd rowProcesos/Metodología
3rd rowProductos/Servicios
4th rowProcesos/Metodología
5th rowGestión/Management

Common Values

ValueCountFrequency (%)
Procesos/Metodología 282
27.3%
Arquitectura/Infraestructura 139
13.5%
Usuario/Cliente 131
12.7%
Red/Conectividad 97
 
9.4%
Tecnología/IT 78
 
7.6%
Gestión/Management 75
 
7.3%
Productos/Servicios 72
 
7.0%
Seguridad 57
 
5.5%
Datos/Análisis 56
 
5.4%
Innovación/Generacional 46
 
4.5%

Length

2025-05-25T19:56:32.736291image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-25T19:56:32.813247image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
ValueCountFrequency (%)
procesos/metodología 282
27.3%
arquitectura/infraestructura 139
13.5%
usuario/cliente 131
12.7%
red/conectividad 97
 
9.4%
tecnología/it 78
 
7.6%
gestión/management 75
 
7.3%
productos/servicios 72
 
7.0%
seguridad 57
 
5.5%
datos/análisis 56
 
5.4%
innovación/generacional 46
 
4.5%

Most occurring characters

ValueCountFrequency (%)
o 2158
 
11.3%
e 1822
 
9.5%
a 1406
 
7.3%
r 1355
 
7.1%
t 1344
 
7.0%
s 1221
 
6.4%
i 1075
 
5.6%
/ 976
 
5.1%
c 971
 
5.1%
n 956
 
5.0%
Other values (22) 5852
30.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2158
 
11.3%
e 1822
 
9.5%
a 1406
 
7.3%
r 1355
 
7.1%
t 1344
 
7.0%
s 1221
 
6.4%
i 1075
 
5.6%
/ 976
 
5.1%
c 971
 
5.1%
n 956
 
5.0%
Other values (22) 5852
30.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2158
 
11.3%
e 1822
 
9.5%
a 1406
 
7.3%
r 1355
 
7.1%
t 1344
 
7.0%
s 1221
 
6.4%
i 1075
 
5.6%
/ 976
 
5.1%
c 971
 
5.1%
n 956
 
5.0%
Other values (22) 5852
30.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2158
 
11.3%
e 1822
 
9.5%
a 1406
 
7.3%
r 1355
 
7.1%
t 1344
 
7.0%
s 1221
 
6.4%
i 1075
 
5.6%
/ 976
 
5.1%
c 971
 
5.1%
n 956
 
5.0%
Other values (22) 5852
30.6%

Interactions

2025-05-25T19:56:27.373585image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:14.241241image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:15.146818image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:16.098284image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:18.271114image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:19.247708image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:20.194518image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:21.384625image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:22.336237image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:23.289270image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:24.199435image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:25.461484image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:26.474850image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:27.441811image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:14.309156image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:15.218731image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:16.167978image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:18.350113image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:19.325184image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:20.268461image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:21.456438image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:22.405423image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:23.357406image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:24.265613image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:25.535378image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:26.558466image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:27.511974image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:14.377280image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:15.312397image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:16.240740image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:18.421972image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:19.395244image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:20.339072image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:21.528991image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:22.477567image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:23.430794image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:24.335746image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:25.610036image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:26.628726image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:27.585755image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:14.447561image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:15.390757image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:16.312441image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:18.496674image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:19.469571image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:20.419213image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:21.605787image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:22.551095image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:23.500384image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:24.408125image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:25.686716image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:26.697631image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:27.653875image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:14.514826image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:15.462331image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:16.384009image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:18.566010image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:19.537482image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:20.488008image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:21.675150image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:22.636721image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:23.568792image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:24.477844image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:25.760316image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:26.763199image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:27.722461image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:14.577834image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:15.532601image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:16.451717image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:18.638065image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:19.625864image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:20.558386image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:21.744698image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:22.710933image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:23.633975image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:24.542945image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:25.832218image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:26.825313image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:27.792560image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:14.644664image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:15.603293image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:16.521591image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:18.709658image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:19.693591image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:20.620475image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:21.813065image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:22.776801image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:23.702111image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:24.609331image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:25.918364image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:26.889935image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:27.862460image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:14.716946image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:15.672395image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:16.595145image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:18.785370image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:19.766550image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:20.687485image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:21.885059image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:22.848903image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:23.771685image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:24.708668image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:25.993652image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:26.956116image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:27.932248image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:14.788184image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:15.741937image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:16.670026image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:18.854816image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:19.835796image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:20.755698image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:21.960402image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:22.914557image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:23.847955image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:24.780649image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:26.069752image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:27.024217image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:28.000002image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:14.863914image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:15.812784image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:16.741658image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:18.939699image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:19.903040image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:20.823608image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:22.032015image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:22.985832image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:23.922133image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:25.166992image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:26.141734image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:27.087654image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:28.070368image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:14.937536image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:15.883594image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:18.031496image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:19.014959image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:19.971140image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:20.892750image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:22.107445image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:23.058388image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:23.990433image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:25.233398image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:26.218133image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:27.172062image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:28.142818image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:15.008317image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:15.955132image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:18.108331image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:19.099334image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:20.052812image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:20.968453image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:22.189997image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:23.140039image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:24.064278image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:25.314673image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:26.293234image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:27.244503image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:28.211493image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:15.074739image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:16.024953image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:18.183038image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:19.169271image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:20.124853image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:21.315087image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:22.262408image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:23.213621image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:24.128548image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:25.388350image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:26.363660image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-05-25T19:56:27.306578image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Correlations

2025-05-25T19:56:32.908641image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
budgetbudget_categoriachannelconversion_categoriaconversion_rateconversionscosto_clickscosto_por_conversionefficiency_indexingresos_por_clicknet_profitrevenuerevenue_categoriarevenue_per_dollarroiroi_categoriaroi_recalculatedstart_monthtarget_audiencetemporada_iniciothemetype
budget1.0000.0000.0000.000-0.015-0.0190.6420.5880.011-0.605-0.115-0.0190.000-0.6670.0270.000-0.667-0.0570.0000.0000.0000.000
budget_categoria0.0001.0000.0000.0000.0580.0000.0000.0000.0000.6550.0000.0000.0000.4420.5740.2640.4420.0490.0000.0140.0470.000
channel0.0000.0001.0000.0340.0220.0170.0000.0000.0360.0150.0000.0050.0530.0160.0560.0210.0160.0890.0000.0740.0000.000
conversion_categoria0.0000.0000.0341.0000.9070.4650.0510.0000.5020.0900.0000.0000.0000.0000.0000.0320.0000.0450.0000.0000.0000.016
conversion_rate-0.0150.0580.0220.9071.0000.6100.481-0.4240.704-0.3450.0120.0110.0140.0250.0350.0180.025-0.0740.0000.0000.0000.000
conversions-0.0190.0000.0170.4650.6101.0000.351-0.7450.4790.2440.7280.7310.5590.5360.0580.0710.536-0.0670.0180.0000.0000.000
costo_clicks0.6420.0000.0000.0510.4810.3511.0000.1720.373-0.616-0.0570.0090.149-0.4530.0200.000-0.453-0.1080.0000.0690.0390.000
costo_por_conversion0.5880.0000.0000.000-0.424-0.7450.1721.000-0.315-0.598-0.645-0.5920.000-0.874-0.0260.000-0.8740.0160.0010.0060.0000.000
efficiency_index0.0110.0000.0360.5020.7040.4790.373-0.3151.000-0.2470.0340.0340.0350.0280.6810.4220.028-0.0910.0600.0580.0000.000
ingresos_por_click-0.6050.6550.0150.090-0.3450.244-0.616-0.598-0.2471.0000.6560.6020.0880.8930.0010.2620.8930.0410.0000.0000.0340.026
net_profit-0.1150.0000.0000.0000.0120.728-0.057-0.6450.0340.6561.0000.9950.0000.7380.0400.0000.738-0.0220.0000.0000.0000.000
revenue-0.0190.0000.0050.0000.0110.7310.009-0.5920.0340.6020.9951.0000.9430.6770.0410.0780.677-0.0290.0000.0000.0330.000
revenue_categoria0.0000.0000.0530.0000.0140.5590.1490.0000.0350.0880.0000.9431.0000.1540.0580.0720.1540.0160.0000.0150.0000.000
revenue_per_dollar-0.6670.4420.0160.0000.0250.536-0.453-0.8740.0280.8930.7380.6770.1541.0000.0210.0161.0000.0150.0430.0000.0250.000
roi0.0270.5740.0560.0000.0350.0580.020-0.0260.6810.0010.0400.0410.0580.0211.0000.9840.021-0.0570.0840.0650.0390.021
roi_categoria0.0000.2640.0210.0320.0180.0710.0000.0000.4220.2620.0000.0780.0720.0160.9841.0000.0160.0420.0000.0310.0000.000
roi_recalculated-0.6670.4420.0160.0000.0250.536-0.453-0.8740.0280.8930.7380.6770.1541.0000.0210.0161.0000.0150.0430.0000.0250.000
start_month-0.0570.0490.0890.045-0.074-0.067-0.1080.016-0.0910.041-0.022-0.0290.0160.015-0.0570.0420.0151.0000.0490.9420.0000.023
target_audience0.0000.0000.0000.0000.0000.0180.0000.0010.0600.0000.0000.0000.0000.0430.0840.0000.0430.0491.0000.0590.0240.038
temporada_inicio0.0000.0140.0740.0000.0000.0000.0690.0060.0580.0000.0000.0000.0150.0000.0650.0310.0000.9420.0591.0000.0000.057
theme0.0000.0470.0000.0000.0000.0000.0390.0000.0000.0340.0000.0330.0000.0250.0390.0000.0250.0000.0240.0001.0000.000
type0.0000.0000.0000.0160.0000.0000.0000.0000.0000.0260.0000.0000.0000.0000.0210.0000.0000.0230.0380.0570.0001.000
2025-05-25T19:56:33.073874image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
budgetroiconversion_raterevenueduracion_diasroi_recalculatedstart_monthnet_profitconversionscosto_por_conversioncosto_clicksingresos_por_clickrevenue_per_dollarefficiency_index
budget1.000-0.049-0.041-0.051NaN-0.0530.000-0.751-0.0390.7950.004-0.043-0.053-0.037
roi-0.0491.0000.0390.040NaN-0.019-0.0600.0610.053-0.050-0.014-0.017-0.0190.678
conversion_rate-0.0410.0391.0000.009NaN0.014-0.0760.0350.616-0.0330.181-0.1890.0140.689
revenue-0.0510.0400.0091.000NaN0.246-0.0290.6980.710-0.102-0.0020.2030.2460.032
duracion_diasNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
roi_recalculated-0.053-0.0190.0140.246NaN1.000-0.0180.2010.186-0.027-0.0960.7811.000-0.006
start_month0.000-0.060-0.076-0.029NaN-0.0181.000-0.019-0.058-0.014-0.058-0.029-0.018-0.074
net_profit-0.7510.0610.0350.698NaN0.201-0.0191.0000.497-0.637-0.0040.1650.2010.048
conversions-0.0390.0530.6160.710NaN0.186-0.0580.4971.000-0.0770.110-0.0150.1860.444
costo_por_conversion0.795-0.050-0.033-0.102NaN-0.027-0.014-0.637-0.0771.000-0.025-0.022-0.027-0.042
costo_clicks0.004-0.0140.181-0.002NaN-0.096-0.058-0.0040.110-0.0251.000-0.091-0.0960.096
ingresos_por_click-0.043-0.017-0.1890.203NaN0.781-0.0290.165-0.015-0.022-0.0911.0000.781-0.135
revenue_per_dollar-0.053-0.0190.0140.246NaN1.000-0.0180.2010.186-0.027-0.0960.7811.000-0.006
efficiency_index-0.0370.6780.6890.032NaN-0.006-0.0740.0480.444-0.0420.096-0.135-0.0061.000
2025-05-25T19:56:33.219712image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
budgetroiconversion_raterevenueduracion_diasroi_recalculatedstart_monthnet_profitconversionscosto_por_conversioncosto_clicksingresos_por_clickrevenue_per_dollarefficiency_index
budget1.0000.027-0.015-0.019NaN-0.667-0.057-0.115-0.0190.5880.642-0.605-0.6670.011
roi0.0271.0000.0350.041NaN0.021-0.0570.0400.058-0.0260.0200.0010.0210.681
conversion_rate-0.0150.0351.0000.011NaN0.025-0.0740.0120.610-0.4240.481-0.3450.0250.704
revenue-0.0190.0410.0111.000NaN0.677-0.0290.9950.731-0.5920.0090.6020.6770.034
duracion_diasNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
roi_recalculated-0.6670.0210.0250.677NaN1.0000.0150.7380.536-0.874-0.4530.8931.0000.028
start_month-0.057-0.057-0.074-0.029NaN0.0151.000-0.022-0.0670.016-0.1080.0410.015-0.091
net_profit-0.1150.0400.0120.995NaN0.738-0.0221.0000.728-0.645-0.0570.6560.7380.034
conversions-0.0190.0580.6100.731NaN0.536-0.0670.7281.000-0.7450.3510.2440.5360.479
costo_por_conversion0.588-0.026-0.424-0.592NaN-0.8740.016-0.645-0.7451.0000.172-0.598-0.874-0.315
costo_clicks0.6420.0200.4810.009NaN-0.453-0.108-0.0570.3510.1721.000-0.616-0.4530.373
ingresos_por_click-0.6050.001-0.3450.602NaN0.8930.0410.6560.244-0.598-0.6161.0000.893-0.247
revenue_per_dollar-0.6670.0210.0250.677NaN1.0000.0150.7380.536-0.874-0.4530.8931.0000.028
efficiency_index0.0110.6810.7040.034NaN0.028-0.0910.0340.479-0.3150.373-0.2470.0281.000
2025-05-25T19:56:33.360210image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
budgetroiconversion_raterevenueduracion_diasroi_recalculatedstart_monthnet_profitconversionscosto_por_conversioncosto_clicksingresos_por_clickrevenue_per_dollarefficiency_index
budget1.0000.018-0.010-0.012NaN-0.505-0.039-0.076-0.0130.4230.498-0.438-0.5050.009
roi0.0181.0000.0240.028NaN0.015-0.0390.0280.038-0.0180.0140.0010.0150.509
conversion_rate-0.0100.0241.0000.008NaN0.017-0.0520.0080.452-0.2940.348-0.2390.0170.522
revenue-0.0120.0280.0081.000NaN0.503-0.0200.9360.558-0.4230.0010.4330.5030.024
duracion_diasNaNNaNNaNNaN1.000NaNNaNNaNNaNNaNNaNNaNNaNNaN
roi_recalculated-0.5050.0150.0170.503NaN1.0000.0110.5660.379-0.708-0.3640.7331.0000.018
start_month-0.039-0.039-0.052-0.020NaN0.0111.000-0.015-0.0470.011-0.0750.0270.011-0.062
net_profit-0.0760.0280.0080.936NaN0.566-0.0151.0000.553-0.470-0.0460.4830.5660.023
conversions-0.0130.0380.4520.558NaN0.379-0.0470.5531.000-0.5710.2450.1650.3790.334
costo_por_conversion0.423-0.018-0.294-0.423NaN-0.7080.011-0.470-0.5711.0000.133-0.448-0.708-0.213
costo_clicks0.4980.0140.3480.001NaN-0.364-0.075-0.0460.2450.1331.000-0.537-0.3640.257
ingresos_por_click-0.4380.001-0.2390.433NaN0.7330.0270.4830.165-0.448-0.5371.0000.733-0.164
revenue_per_dollar-0.5050.0150.0170.503NaN1.0000.0110.5660.379-0.708-0.3640.7331.0000.018
efficiency_index0.0090.5090.5220.024NaN0.018-0.0620.0230.334-0.2130.257-0.1640.0181.000
2025-05-25T19:56:33.512057image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
budgetroitypetarget_audiencechannelconversion_raterevenueroi_categoriaconversion_categoriarevenue_categoriabudget_categoriaroi_recalculatedstart_monthtemporada_inicionet_profitconversionscosto_por_conversioncosto_clicksingresos_por_clickrevenue_per_dollarefficiency_indextheme
budget1.0000.0950.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0430.0000.7060.0001.0000.0000.0000.0000.0000.000
roi0.0951.0000.0520.1110.0940.0690.1690.9780.0000.0990.7640.0530.1650.1080.0950.1620.0630.0000.6530.0530.7040.125
type0.0000.0521.0000.0310.0000.0000.0000.0000.0220.0000.0000.0000.0560.0700.0000.0000.0000.0000.0420.0000.0000.000
target_audience0.0000.1110.0311.0000.0000.0000.0000.0000.0000.0000.0000.0580.0650.0890.0000.0240.0010.0000.0000.0580.0790.031
channel0.0000.0940.0000.0001.0000.0490.0090.0220.0360.0560.0000.0360.1490.1850.0000.0280.0000.0000.0220.0360.0610.000
conversion_rate0.0000.0690.0000.0000.0491.0000.0400.0280.9160.0220.1300.0000.0610.0000.0000.5620.0000.1070.1430.0000.7570.000
revenue0.0360.1690.0000.0000.0090.0401.0000.1320.0000.9520.0000.1600.1360.0000.0360.7640.0470.3330.1070.1600.0680.106
roi_categoria0.0000.9780.0000.0000.0220.0280.1321.0000.1070.2270.2760.0260.0710.0330.0000.1190.0000.0000.3630.0260.5800.000
conversion_categoria0.0000.0000.0220.0000.0360.9160.0000.1071.0000.0000.0000.0000.0770.0000.0000.6210.0000.0820.1350.0000.6550.000
revenue_categoria0.0000.0990.0000.0000.0560.0220.9520.2270.0001.0000.0000.2370.0270.0160.0000.7030.0000.2290.1310.2370.0590.000
budget_categoria0.0000.7640.0000.0000.0000.1300.0000.2760.0000.0001.0000.7760.0830.0360.0000.0000.0000.0000.7700.7760.0000.079
roi_recalculated0.0000.0530.0000.0580.0360.0000.1600.0260.0000.2370.7761.0000.0000.0000.0000.1620.0000.0000.7401.0000.0000.053
start_month0.0430.1650.0560.0650.1490.0610.1360.0710.0770.0270.0830.0001.0000.9870.0430.0000.0000.1700.0970.0000.1530.000
temporada_inicio0.0000.1080.0700.0890.1850.0000.0000.0330.0000.0160.0360.0000.9871.0000.0000.0000.0070.1540.0000.0000.0970.000
net_profit0.7060.0950.0000.0000.0000.0000.0360.0000.0000.0000.0000.0000.0430.0001.0000.0001.0000.0000.0000.0000.0000.000
conversions0.0000.1620.0000.0240.0280.5620.7640.1190.6210.7030.0000.1620.0000.0000.0001.0000.0000.1000.0000.1620.5180.000
costo_por_conversion1.0000.0630.0000.0010.0000.0000.0470.0000.0000.0000.0000.0000.0000.0071.0000.0001.0000.0000.0000.0000.0000.000
costo_clicks0.0000.0000.0000.0000.0000.1070.3330.0000.0820.2290.0000.0000.1700.1540.0000.1000.0001.0000.0000.0000.1800.082
ingresos_por_click0.0000.6530.0420.0000.0220.1430.1070.3630.1350.1310.7700.7400.0970.0000.0000.0000.0000.0001.0000.7400.0330.069
revenue_per_dollar0.0000.0530.0000.0580.0360.0000.1600.0260.0000.2370.7761.0000.0000.0000.0000.1620.0000.0000.7401.0000.0000.053
efficiency_index0.0000.7040.0000.0790.0610.7570.0680.5800.6550.0590.0000.0000.1530.0970.0000.5180.0000.1800.0330.0001.0000.000
theme0.0000.1250.0000.0310.0000.0000.1060.0000.0000.0000.0790.0530.0000.0000.0000.0000.0000.0820.0690.0530.0001.000
2025-05-25T19:56:33.661929image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
budget_categoriachannelconversion_categoriarevenue_categoriaroi_categoriatarget_audiencetemporada_iniciothemetype
budget_categoria1.0000.0000.0000.0000.2640.0000.0140.0470.000
channel0.0001.0000.0340.0530.0210.0000.0740.0000.000
conversion_categoria0.0000.0341.0000.0000.0320.0000.0000.0000.016
revenue_categoria0.0000.0530.0001.0000.0720.0000.0150.0000.000
roi_categoria0.2640.0210.0320.0721.0000.0000.0310.0000.000
target_audience0.0000.0000.0000.0000.0001.0000.0590.0240.038
temporada_inicio0.0140.0740.0000.0150.0310.0591.0000.0000.057
theme0.0470.0000.0000.0000.0000.0240.0001.0000.000
type0.0000.0000.0160.0000.0000.0380.0570.0001.000

Missing values

2025-05-25T19:56:28.337992image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-25T19:56:28.492351image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

campaign_namebudgetroitypetarget_audiencechannelconversion_raterevenueduracion_diasroi_categoriaconversion_categoriarevenue_categoriabudget_categoriaroi_recalculatedstart_monthtemporada_inicionet_profitconversionscosto_por_conversioncosto_clicksingresos_por_clickrevenue_per_dollarefficiency_indextheme
0Public-key multi-tasking throughput8082.300.35emailb2borganic0.40709593.481BajoBajaMuchaMedio86.7959844Primavera701511.18283837.39200.0284753270.167346216.98996087.7959840.1400Usuario/Cliente
1De-engineered analyzing task-force17712.980.74emailb2cpromotion0.66516609.101MedioMediaMediaAlto28.1655672Invierno498896.12340962.00600.05195012105.63271842.67510129.1655670.4884Procesos/Metodología
2Balanced solution-oriented Local Area Network84643.100.37podcastb2bpaid0.28458227.421BajoBajaMediaMuy Alto4.41364212Invierno373584.32128303.67760.65970929069.79879015.7630065.4136420.1036Productos/Servicios
3Distributed real-time methodology14589.750.47webinarb2borganic0.1989958.731BajoBajaPocaAlto5.1658869Otoño75368.9817092.15870.8535933308.65990727.1888726.1658860.0893Procesos/Metodología
4Front-line executive infrastructure39291.900.30social mediab2bpromotion0.8147511.351BajoAltaPocaAlto0.2091897Verano8219.4538484.19351.020988183968.1587680.2582591.2091890.2430Gestión/Management
5Upgradable transitional data-warehouse75569.280.59social mediab2creferral0.67558302.111MedioMediaMediaMuy Alto6.3879516Verano482732.83374062.41370.20202358557.4991429.5342557.3879510.3953Datos/Análisis
6Innovative context-sensitive framework28964.450.59emailb2creferral0.17172882.591MedioBajaPocaAlto4.9687863Primavera143918.1429390.04030.9855195914.93437029.2281505.9687860.1003Innovación/Generacional
7User-friendly client-driven service-desk36800.580.40webinarb2cpromotion0.52206241.461BajoMediaPocaAlto4.6042991Invierno169440.88107245.55920.34314323292.4827888.8544215.6042990.2080Usuario/Cliente
8Proactive neutral methodology40493.880.16webinarb2corganic0.47734755.761BajoMediaMuchaAlto17.1448599Otoño694261.88345335.20720.11726020142.20115336.47842418.1448590.0752Procesos/Metodología
9Intuitive responsive support1816.220.81social mediab2creferral0.85563280.301MedioAltaMediaMedio309.13880511Otoño561464.08478788.25500.0037931548.780831363.692711310.1388050.6885Usuario/Cliente
campaign_namebudgetroitypetarget_audiencechannelconversion_raterevenueduracion_diasroi_categoriaconversion_categoriarevenue_categoriabudget_categoriaroi_recalculatedstart_monthtemporada_inicionet_profitconversionscosto_por_conversioncosto_clicksingresos_por_clickrevenue_per_dollarefficiency_indextheme
1023Broken-date campaign25000.000.45emailb2borganic0.5587500.0001BajoMediaPocaAlto2.5000001Invierno62500.00048125.000000.51948119250.0000004.5454553.5000000.2475Procesos/Metodología
1024Negative ROI test-10000.00-0.20podcastb2creferral0.10517944.0351PérdidaBajaMediaBajo-52.79440410Otoño527944.03551794.40350-0.193071-981.058598-527.944035-51.794404-0.0200Datos/Análisis
1025Null-heavy campaign46919.950.53emailb2bpromotion0.55517944.0351MedioMediaMediaAlto10.0388871Invierno471024.085284869.219250.16470728376.57340518.25252211.0388870.2915Gestión/Management
1026Future campaign75000.000.90webinarb2cpromotion0.65200000.0001MedioMediaPocaMuy Alto1.6666671Invierno125000.000130000.000000.57692378000.0000002.5641032.6666670.5850Innovación/Generacional
1027Extra long name campaign test30000.000.25emailb2bpaid0.4045000.0001BajoBajaPocaAlto0.5000004Primavera15000.00018000.000001.66666736000.0000001.2500001.5000000.1000Procesos/Metodología
1028No revenue campaign20000.000.30social mediab2borganic0.50517944.0351BajoMediaMediaAlto24.8972022Invierno497944.035258972.017500.07722810401.65156349.79440425.8972020.1500Datos/Análisis
1029Random mess100000.000.53podcastb2breferral0.55300000.0001MedioMediaPocaMuy Alto2.0000006Verano200000.000165000.000000.60606182500.0000003.6363643.0000000.2915Red/Conectividad
1030Invalid budget46919.950.53emailb2cpromotion0.2050000.0001MedioBajaPocaAlto0.06564512Invierno3080.05010000.000004.691995152335.0270290.3282241.0656450.1060Red/Conectividad
1031Overlapping dates60000.000.60webinarb2bpaid0.7090000.0001MedioMediaPocaMuy Alto0.5000003Primavera30000.00063000.000000.952381126000.0000000.7142861.5000000.4200Arquitectura/Infraestructura
1032Too many conversions40000.000.80social mediab2corganic1.50120000.0001MedioAltaPocaAlto2.0000005Primavera80000.000180000.000000.22222290000.0000001.3333333.0000001.2000Procesos/Metodología
campaign_namebudgetroitypetarget_audiencechannelconversion_raterevenueduracion_diasroi_categoriaconversion_categoriarevenue_categoriabudget_categoriaroi_recalculatedstart_monthtemporada_inicionet_profitconversionscosto_por_conversioncosto_clicksingresos_por_clickrevenue_per_dollarefficiency_indextheme
461Robust disintermediate neural-net92395.780.37social mediab2corganic0.31949696.651BajoBajaMuchaMuy Alto9.2785724Primavera857300.87294405.96150.31383831729.66388029.93087610.2785720.1147Tecnología/IT
629Reduced didactic leverage12895.480.95social mediab2breferral0.90619182.581MedioAltaMediaAlto47.0154748Verano606287.10557264.32200.02314111852.78545352.23941548.0154740.8550Procesos/Metodología
966Networked upward-trending forecast43185.330.47emailb2breferral0.72918409.281BajoAltaMuchaAlto20.2666967Verano875223.95661254.68160.06530832627.65105928.14818921.2666960.3384Red/Conectividad
826Quality-focused local methodology11092.230.54social mediab2cpaid0.83104296.761MedioAltaPocaAlto8.4026869Otoño93204.5386566.31080.12813610302.21846110.1237199.4026860.4482Procesos/Metodología
198Horizontal 6thgeneration model16308.370.65webinarb2cpromotion0.37511344.871MedioBajaMediaAlto30.35475011Otoño495036.50189197.60190.0861986232.88281882.03986631.3547500.2405Procesos/Metodología

Duplicate rows

Most frequently occurring

campaign_namebudgetroitypetarget_audiencechannelconversion_raterevenueduracion_diasroi_categoriaconversion_categoriarevenue_categoriabudget_categoriaroi_recalculatedstart_monthtemporada_inicionet_profitconversionscosto_por_conversioncosto_clicksingresos_por_clickrevenue_per_dollarefficiency_indextheme# duplicates
0Balanced solution-oriented Local Area Network84643.100.37podcastb2bpaid0.28458227.421BajoBajaMediaMuy Alto4.41364212Invierno373584.32128303.67760.65970929069.79879015.7630065.4136420.1036Productos/Servicios3
2De-engineered analyzing task-force17712.980.74emailb2cpromotion0.66516609.101MedioMediaMediaAlto28.1655672Invierno498896.12340962.00600.05195012105.63271842.67510129.1655670.4884Procesos/Metodología3
3Distributed real-time methodology14589.750.47webinarb2borganic0.1989958.731BajoBajaPocaAlto5.1658869Otoño75368.9817092.15870.8535933308.65990727.1888726.1658860.0893Procesos/Metodología3
9Public-key multi-tasking throughput8082.300.35emailb2borganic0.40709593.481BajoBajaMuchaMedio86.7959844Primavera701511.18283837.39200.0284753270.167346216.98996087.7959840.1400Usuario/Cliente3
1Cross-platform demand-driven encoding64041.370.16social mediab2bpromotion0.55174462.471BajoMediaPocaMuy Alto1.7242157Verano110421.1095954.35850.66741555651.0356793.1349372.7242150.0880Arquitectura/Infraestructura2
4Front-line executive infrastructure39291.900.30social mediab2bpromotion0.8147511.351BajoAltaPocaAlto0.2091897Verano8219.4538484.19351.020988183968.1587680.2582591.2091890.2430Gestión/Management2
5Innovative context-sensitive framework28964.450.59emailb2creferral0.17172882.591MedioBajaPocaAlto4.9687863Primavera143918.1429390.04030.9855195914.93437029.2281505.9687860.1003Innovación/Generacional2
6Intuitive responsive support1816.220.81social mediab2creferral0.85563280.301MedioAltaMediaMedio309.13880511Otoño561464.08478788.25500.0037931548.780831363.692711310.1388050.6885Usuario/Cliente2
7Multi-lateral dedicated workforce94084.210.58podcastb2breferral0.23406522.771MedioBajaMediaMuy Alto3.3208396Verano312438.5693500.23711.00624628155.60263214.4384334.3208390.1334Gestión/Management2
8Proactive neutral methodology40493.880.16webinarb2corganic0.47734755.761BajoMediaMuchaAlto17.1448599Otoño694261.88345335.20720.11726020142.20115336.47842418.1448590.0752Procesos/Metodología2